An Efficient Security Model for Industrial Internet of Things (IIoT) System Based on Machine Learning Principles

TL;DR

This paper introduces a machine learning-based security framework for IIoT edge devices, enabling threat detection and authentication while balancing model performance and computational constraints.

Contribution

It proposes a novel methodology for deploying trained ML models on low-performance edge devices, enhancing security without sacrificing efficiency.

Findings

Effective detection of MQTT-based attacks using ML models

Validation of data authenticity and source integrity techniques

Applicable to low-cost single-board computers

Abstract

This paper presents a security paradigm for edge devices to defend against various internal and external threats. The first section of the manuscript proposes employing machine learning models to identify MQTT-based (Message Queue Telemetry Transport) attacks using the Intrusion Detection and Prevention System (IDPS) for edge nodes. Because the Machine Learning (ML) model cannot be trained directly on low-performance platforms (such as edge devices),a new methodology for updating ML models is proposed to provide a tradeoff between the model performance and the computational complexity. The proposed methodology involves training the model on a high-performance computing platform and then installing the trained model as a detection engine on low-performance platforms (such as the edge node of the edge layer) to identify new attacks. Multiple security techniques have been employed in the second half of the manuscript to verify that the exchanged trained model and the exchanged data files are valid and undiscoverable (information authenticity and privacy) and that the source (such as a fog node or edge device) is indeed what it it claimed to be (source authentication and message integrity). Finally, the proposed security paradigm is found to be effective against various internal and external threats and can be applied to a low-cost single-board computer (SBC).